File size: 1,450 Bytes
4f65a7c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | # -*- coding: utf-8 -*-
"""Zadanie4_Semenov_II_DRPK47.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/11fNvzrVniDSjEVdE-ZnejpPvvA88ApfY
"""
import numpy as np
import matplotlib.pyplot as plt
import tensorflow.keras as keras
import tensorflow.keras.datasets
from tensorflow.keras.datasets import fashion_mnist
from tensorflow.keras.layers import Input, Dense
(train_x, train_y), (test_x, test_y) = fashion_mnist.load_data()
train_x = train_x / 255
test_x = test_x / 255
train_x = np.reshape(train_x, (len(train_x), 28 * 28))
test_x = np.reshape(test_x, (len(test_x), 28 * 28))
inputs = Input(shape = (28*28, ))
x = Dense(150, activation = 'relu')(inputs)
x = Dense(400, activation = 'relu')(x)
x = Dense(10, activation = 'relu')(x)
encoder = Dense(3, activation = 'linear')(x)
inputs_dec = Input(shape = (3, ))
x = Dense(10, activation = 'relu')(inputs_dec)
x = Dense(40, activation = 'relu')(x)
x = Dense(150, activation = 'relu')(x)
decoder = Dense(28*28, activation = 'relu')(x)
encoder_model = keras.Model(inputs, encoder)
decoder_model = keras.Model(inputs_dec, decoder)
autoenc = keras.Model(inputs, decoder_model(encoder_model(inputs)))
autoenc.compile(optimizer='adam', loss='mean_squared_error', metrics = ['accuracy'])
autoenc.fit(train_x, train_x, epochs = 20, batch_size=50)
y = autoenc.predict(test_x[:12])
plt.imshow(y[5].reshape(28, 28), cmap = 'gray') |